Image data compression and decompression

ABSTRACT

A method of processing image data includes obtaining an image set that includes at least a first image and a second image, determining a deformation registration using the first and second images, wherein the act of determining the deformation registration is performed using a processor, performing image compression on at least a portion of the image set using the determined deformation registration to obtain compressed image data, and storing the compressed image data. A method of processing image data includes obtaining compressed image data, obtaining a deformation registration previously used to create the compressed image data, performing image decompression on the compressed image data using the deformation registration to obtain decompressed image data, wherein the act of performing the image decompression is performed using a processor, and storing the decompressed image data.

FIELD

This application relates generally to image data processing, and moreparticularly, to systems and methods for compressing and decompressingmedical image data, such as those obtained using radiation, MRI, etc.

BACKGROUND

Radiation has been employed to obtain images of patients for medicalpurposes. For example, in a computed tomography (CT) procedure, adiagnostic energy beam is applied from an external source towards thepatient from different gantry angles to obtain different respectiveprojection images. The projection images may then be used to reconstructa volumetric image. The volumetric image may be used to examine tissuefor diagnosis purpose and/or for treatment planning purpose.

In some cases, different volumetric images for different respectivephases of a physiological cycle are obtained, and the differentvolumetric images may be displayed in a time or phase sequence to forman animated image, or movie (4D CT). Such a movie allows a physician ora treatment planner to see how different tissues move as the patientexperiences physiological movement, thereby allowing treatment planningto incorporate patient movement. Procedures for 4D CT involve obtainingprojection image data for different phases of a motion cycle (e.g.,respiratory, cardiac), and then using the image data to reconstructvolumetric images for different phases. The resulting images may bedisplayed in a sequence to form a video.

Currently, the entire image data set for a volumetric image is stored.For the case of a 4D CT image set, the entire image set is also stored.For the case of a sequentially registered 4D CT image set, registrationmatrices connecting each phase are also stored. Applicant of the subjectapplication determines that individually storing each of the pixelvalues in a volumetric image, or individually storing each of the pixelvalues in each of the volumetric images in a movie along with theregistration matrices, requires significant storage space. Thus,Applicant determines that a system and method for compressing anddecompressing image data would be desirable.

SUMMARY

In accordance with some embodiments, a method of processing image dataincludes obtaining an image set that includes at least a first image anda second image, determining a deformation registration using the firstand second images, wherein the act of determining the deformationregistration is performed using a processor, performing imagecompression on at least a portion of the image set using the determineddeformation registration to obtain compressed image data, and storingthe compressed image data.

In accordance with other embodiments, a system for processing image dataincludes a processor configured for obtaining an image set that includesat least a first image and a second image, determining a deformationregistration using the first and second images, performing imagecompression on at least a portion of the image set using the determineddeformation registration to obtain compressed image data, and storingthe compressed image data.

In accordance with other embodiments, a computer product having avolatile or non-volatile medium that stores a set of instruction, anexecution of which by a processor causes a method for processing imagedata to be performed, the method includes obtaining an image set thatincludes at least a first image and a second image, determining adeformation registration using the first and second images, performingimage compression on at least a portion of the image set using thedetermined deformation registration to obtain compressed image data, andstoring the compressed image data.

In accordance with other embodiments, a method of processing image dataincludes obtaining compressed image data, obtaining a deformationregistration previously used to create the compressed image data,performing image decompression on the compressed image data using thedeformation registration to obtain decompressed image data, wherein theact of performing the image decompression is performed using aprocessor, and storing the decompressed image data.

In accordance with other embodiments, a system for processing image dataincludes a processor configured for obtaining compressed image data,obtaining a deformation registration previously used to create thecompressed image data, performing image decompression on the compressedimage data using the deformation registration to obtain decompressedimage data; and storing the decompressed image data.

In accordance with other embodiments, a computer product having avolatile or non-volatile medium that stores a set of instruction, anexecution of which by a processor causes a method for processing imagedata to be performed, the method includes obtaining compressed imagedata, obtaining a deformation registration previously used to create thecompressed image data, performing image decompression on the compressedimage data using the deformation registration to obtain decompressedimage data, and storing the decompressed image data.

Other and further aspects and features will be evident from reading thefollowing detailed description of the embodiments, which are intended toillustrate, not limit, the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate the design and utility of embodiments, in whichsimilar elements are referred to by common reference numerals. Thesedrawings are not necessarily drawn to scale. In order to betterappreciate how the above-recited and other advantages and objects areobtained, a more particular description of the embodiments will berendered, which are illustrated in the accompanying drawings. Thesedrawings depict only typical embodiments and are not therefore to beconsidered limiting of its scope.

FIG. 1 illustrates a system for obtaining image(s) of a patient inaccordance with some embodiments;

FIG. 2 illustrates a method of compressing image data that involvesdeformation registration in accordance with some embodiments;

FIG. 3 illustrate a concept of compressing image data that involvesdeformation registration in accordance with some embodiments;

FIG. 4 illustrates another technique of compressing image data;

FIG. 5 illustrate another concept of compressing image data thatinvolves deformation registration in accordance with some embodiments;

FIGS. 6A-6C illustrate a method of compressing image data in accordancewith some embodiments;

FIGS. 7A and 7B illustrate another method of compressing image data inaccordance with other embodiments;

FIG. 8 illustrates a method of decompressing image data that involvesdeformation registration in accordance with some embodiments; and

FIG. 9 is a block diagram of a computer system architecture, with whichembodiments described herein may be implemented.

DESCRIPTION OF THE EMBODIMENTS

Various embodiments are described hereinafter with reference to thefigures. It should be noted that the figures are not drawn to scale andthat elements of similar structures or functions are represented by likereference numerals throughout the figures. It should also be noted thatthe figures are only intended to facilitate the description of theembodiments. They are not intended as an exhaustive description of theinvention or as a limitation on the scope of the invention. In addition,an illustrated embodiment needs not have all the aspects or advantagesshown. An aspect or an advantage described in conjunction with aparticular embodiment is not necessarily limited to that embodiment andcan be practiced in any other embodiments even if not so illustrated.

FIG. 1 illustrates a radiation imaging system 10 for obtaining image(s)of a patient in accordance with some embodiments. As used in thisspecification, the term “image” is not limited to an image that isdisplayed, and may refer to image data that is not displayed, such asimage data that is stored in a medium. The system 10 includes a gantry12 (in the form of a ring), a patient support 14 for supporting apatient 28, and a control system 18 for controlling an operation of thegantry 12. In other embodiments, the radiation source 20 may be coupledto an arm gantry instead of the ring gantry 12. The system 10 alsoincludes a radiation source 20 that projects a beam 26 of radiationtowards the patient 28 while the patient 28 is supported on the support14. The radiation source 20 can be configured to generate a cone beam, afan beam, or other types of radiation beams in different embodiments.

In the illustrated embodiments, the radiation source 20 is a diagnosticradiation source for providing imaging energy. In other embodiments, inaddition to being a diagnostic radiation source, the radiation source 20can also be a treatment radiation source for providing treatment energy.In some embodiments, the treatment energy is generally those energies of160 kilo-electron-volts (keV) or greater, and more typically 1mega-electron-volts (MeV) or greater, and diagnostic energy is generallythose energies below the high energy range, and more typically below 160keV. In other embodiments, the treatment energy and the diagnosticenergy can have other energy levels, and refer to energies that are usedfor treatment and diagnostic purposes, respectively. In someembodiments, the radiation source 20 is able to generate X-ray radiationat a plurality of photon energy levels within a range anywhere betweenapproximately 10 keV and approximately 20 MeV.

The system 10 also include an imager 50 located at an operative positionrelative to the source 20. The imager 50 is configured to receiveradiation that has passed through the patient 28, and generate imagesignals in response to the received radiation. The image signals may betransmitted to a processor 54 (or another processor) for processing inaccordance with embodiments described herein. As used in thisspecification, the term “processor” is not limited to a processingdevice that is coupled to an imaging system, and may refer to anyprocessing device that is capable of performing data processing, whereinthe processing device may include one or more processing units. Forexample, in other embodiments, the processor may be a part of a computerthat is separate from the image acquisition device 10, or may be a partof a PDA. In some embodiments, the image signals from the imager 50 maybe stored in a medium, such as a memory, a CD ROM, a database, etc., forlater processing.

In the illustrated embodiments, the control system 18 includes aprocessor 54, such as a computer processor, coupled to a control 40. Thecontrol system 18 may also include a monitor 56 for displaying data andan input device 58, such as a keyboard or a mouse, for inputting data.In the illustrated embodiments, the gantry 12 is rotatable about thepatient 16, and during an imaging procedure, the gantry 12 rotates aboutthe patient 16 to deliver diagnostic radiation from different gantryangles. The operation of the radiation source 20 and the gantry 12 arecontrolled by the control 40, which provides power and timing signals tothe radiation source 20, and controls a rotational speed and position ofthe gantry 12, based on signals received from the processor 54. Thecontrol 40 may also be configured to provide timing signals foraccessing image signals from the imager 50. Although the control 40 isshown as a separate component from the gantry 12 and the processor 54,in alternative embodiments, the control 40 can be a part of the gantry12 or the processor 54.

It should be noted that the system 10 is not limited to theconfiguration described above, and that the system 10 may have otherconfigurations in other embodiments. For example, in other embodiments,the system 10 may have a different shape. In other embodiments, theradiation source 20 of the system 10 may have different ranges ofmotions and/or degrees of freedom. For example, in other embodiments,the radiation source 20 may be rotatable about the patient 28 completelythrough a 360° range, or partially through a range that is less than360°. Also, in other embodiments, the radiation source 20 istranslatable relative to the patient 28. Further, the radiation source20 is not limited to delivering treatment energy in the form of x-ray,and may deliver other types of radiation energy.

In some embodiments, the imaging system 10 may be configured to obtainprojection data from different gantry angles that is sufficient forconstructing a volumetric image (e.g., a CT image) for at least a partof the patient 28. In other embodiments, if the portion of the patient28 that is being imaged moves (e.g., due to breathing, cardiac movement,etc.), then the imaging system 10 may be configured to obtain projectiondata that is sufficient for constructing volumetric images for differentphases of the physiological cycle of the patient 28. In such cases,projection data that correspond to the same phase (or phase range) ofthe physiological are grouped together for construction of thevolumetric image for that particular phase (or phase range). Thedifferent volumetric images for different respect phases of thephysiological cycle may then be displayed in a sequence to form a movie(video). If cone beam radiation is used to obtain the sequence ofvolumetric images, the resulting set of images is a four dimensional(4D) CBCT set, with the fourth dimension being time. In someembodiments, the set of volumetric images may be transmitted to aprocessor (such as processor 54) for processing in accordance withembodiments described herein. In other embodiments, the set ofvolumetric images may be stored in a medium for later processing.

In the above embodiments, the system 10 has been described as a CTsystem. In other embodiments, the system 10 may be other types ofimaging systems, such as a MRI system, an ultrasound system, an x-raysystem, a PET system, a SPECT system, etc. In such cases, the image datagenerated by the system 10 may be other types of images, such as MRIimages, ultrasound images, x-ray images, PET images, SPECT images, etc,or any images that are capable of being digitized.

FIG. 2 illustrates a method 200 of compressing image data that involvesdeformation registration in accordance with some embodiments. First, animage set that includes at least a first image and a second image isobtained (Step 202). In some embodiments, such may be accomplished by aprocessor (such as the processor 54) receiving the image set, or byallowing the processor to have access to a medium that stores the imageset. In some embodiments, the image set may include image data for avolumetric image (e.g., a CT image, a tomosynthesis image, etc.). Insuch cases, the first and second images may be different image slices ofthe volumetric image. In other embodiments, the image set may includeimage data that correspond with different respective phases of aphysiological cycle (such as a breathing cycle, a cardiac cycle, etc.)of a patient. In such cases, the first and second images may bedifferent frames from a movie. Each of the frames from the movie may bea two-dimensional image, or a three-dimensional image. In furtherembodiments, the first and second frames may be images obtained atdifferent times. As used in this specification, the terms “first” and“second” (as in “first frame” and “second frame”, or “first image” and“second image”) are used to identify different images, and may notnecessarily indicate the temporal ordering of the images. For example,the “first frame” may be a third image slice in the image set, and the“second frame” may be a second image slice in the image set.

Next, the processor determines a deformation registration using thefirst and second frames (Step 204). Deformable image registration is aprocess or technique in which points in a first frame of a first object(such as a target region and/or a critical organ) are associated withcorresponding points in a second frame of a second object, wherein thefirst and second objects may have the same or different sizes and/orshapes. FIG. 3 illustrates an example of how deformation registrationmay be applied to a volumetric image 300 that includes three imageslices 302 a-302 c. The image slices 302 a-302 c are arranged in theZ-direction, which corresponds with the axis of a bore of an imagingmachine (such as the device of FIG. 1) in some embodiments. Althoughthree image slices are shown, and each slice is illustrated as havingsixteen pixels, in other examples, there may be more or less than threeimage slices, and there may be more or less than sixteen pixels. In theexample, the image slice 302 a has an image of an object 320 b (definedby pixel values that are higher than 0). In particular, the object 320 ahas pixel values of 1 and 2. In other examples, the object 320 a mayhave different shapes, and/or may have pixels with different values fromthat shown. As shown in the figure, the object 320 b in the second imageslice 302 b (e.g., a second image) is shifted to the right by 1 pixelrelative to the first image slice 302 a (e.g., a first image). The imageof the object 320 c in the image slices 302 c stays in the same positionin the image frame relative to the image slice 302 b. Thus, in theillustrated embodiments, the processor may determine that the deformableregistration for mapping between the image slice 302 a and the imageslice 302 b includes a rigid translation of ΔX=1, and that thedeformable registration for mapping between the image slice 302 b andthe image slice 302 c includes no rigid translation (ΔX=0).

Next, the processor performs image compression on at least a portion ofthe image set using the determined deformation registration to obtaincompressed image data (Step 206). In some embodiments, the imagecompression may be performed by compressing image data across imageslices in the Z-direction (or any of other directions that are differentfrom the Z-direction). In the above example, since the pixel 324 a inthe image slice 302 a is associated with the pixel 324 b in the imageslice 302 b based on the determined deformation registration (ΔX=1), theprocessor may store the pixel value for pixel 324 a once, and use thedeformation registration to keep track that the corresponding pixel 324b in the image slice 302 b is shifted in the X-direction by 1 pixelrelative to the adjacent slice 302 a. Thus, this obviates the need tostore multiple pixel values across different image slices. Also, in theabove example, since the pixel 324 b in the image slice 302 b isassociated with the pixel 324 c in the image slice 302 c, the processormay use the deformation registration (ΔX=0) to keep track that the pixel324 c in the image slice 302 c does not move relative to thecorresponding pixel 324 b in the adjacent image slice 302 b.Accordingly, the processor 54 may store the value “0” to keep track ofthe relative shift without storing the pixel value.

In the above example, the configuration (e.g., shape and size) of theobject 320 (defined by pixel values that are higher than 0) does notchange from slice 302 a to slice 302 b to slice 302 c. Thus, thedeformation registration for these image slices (or for componentswithin the image slices) includes only a rigid translation component. Inthe above example, the processor may determine that the compressed dataset includes pixel values for the object 320 a in image slice 302 a(reference image), and the translation components (e.g., ΔX=1, ΔX=0) forthe respective image slices 302 b, 302 c (Step 206). The processor thenstores the compressed image set in a medium (Step 208). As shown in theabove example, the image data compression technique obviates the need tostore each of the pixel values for different respective image slices 302a-302 b.

In some embodiments, the pixel value may be allowed to vary within aprescribed range, and still be considered to be in correspondence withthe pixel in another image for image compression purpose. In the aboveexample, the pixel value for pixel P_(3,2,2) (with convention P_(x,y,z))in the second image slice 302 b is “2.1,” but may still be considered tobe in correspondence with the pixels P_(2,2,1) and P_(3,2,3) (eachhaving a value of “2”) in the adjacent slices 302 a, 302 c. In someembodiments, a user interface may be provided that allows a user toprescribe a deviation range for associating pixels. For example, if theuser prescribes that the deviation range is 4, then a pixel will bedetermined by the processor to be in correspondence with another pixelif the difference in pixel values is less than 4.

As illustrated in the above example, by registering different imageportions from different images using deformable image registrationtechnique, compression of image data may be achieved efficiently basedon the registration of image portions from different images. Inparticular, as can be seen from the above example, by determining thatthe pixels P_(2,2,1), P_(3,2,2), and P_(3,2,3) from respective images302 a-302 c correspond to each other (e.g., with values that belong tothe same class of tissue, or with values that are all within aprescribed range), the compression in the Z-direction may be performedacross these three pixels, even though they do not align with eachother—i.e., the pixel P_(2,2,1) is at a different position with respectto an image frame from the pixel P_(3,2,2) and the pixel P_(3,2,3). Suchtechnique is advantageous over analyzing image pixels that are at thesame spatial location in each image frame for image data compression(see FIG. 4). FIG. 4 shows the same volumetric image 300 with the sameimage slices 302 a-302 c. As illustrated in the figure, if deformableregistration is not used, then performing image compression across theslices (e.g., in the Z-direction) would involve analyzing pixel valuesthat are at the same spatial position in each slice. In the example, thepixel P_(2,2,1) in the slice 302 a has a value of 2. However, the pixelsP_(2,2,2) and P_(2,2,3) that are in the same spatial position relativeto the image frame have values of 0. So the value of P_(2,2,1) is notgrouped with the values of P_(2,2,2) and P_(2,2,3) for image datacompression. On the other hand, by using the deformable registrationtechnique described herein, the processor will recognize that the pixelP_(2,2,1) is associated with pixels P_(3,2,2) and P_(3,2,3) (becausethey have the same value or are within a same value range), and willgroup them together for the purpose of image data compression.

In the above example, the deformation registration is described hashaving one local translation for an image portion within the image.However, in other examples, the deformation registration may have morethan one translation component. For example, in other embodiments, thedeformation registration may have local translation components for animage portion in any one or any combination of the three axes X, Y, Z.

It should be noted that the deformation registration is not limited tohaving only translation(s). In other embodiments, the deformationregistration determined by the processor may include a rotationcomponent that maps how an image portion is rotated to achieve anotherimage portion. In some cases, an image portion may have more than onerotation component. For example, a part of an image portion may have apositive rotation component (representing the condition that the part ofan image portion needs to be rotated clockwise to map to another part ofanother image portion), and a negative rotation component (representingthe condition that another part of the image portion needs to be rotatedcounter-clockwise to map to another respective part of the other imageportion).

In further embodiments, the deformation registration may include one ormore scaling components for an image portion. For example, thedeformation registration may include a scaling component for an imageportion in a X-direction, and another scaling component for the imageportion in the Y-direction. A scaling component with a value greaterthan 1 represents the situation in which the image portion has beenstretched. A scaling component with a value less than 1 represents thesituation in which the image portion has been compressed.

In some cases, the deformation registration for an image portion mayinclude any combination of translation component(s), rotationcomponent(s), stretching component(s), and compression component(s). Thedeformation registration for completely mapping two images may includedifferent deformation registration components for different imageportions. In such cases, each deformation registration components foreach image portion may include any combination of translationcomponent(s), rotation component(s), stretching component(s), andcompression component(s).

As discussed, in some embodiments, instead of, or in addition to, havinga translation and/or rotation component(s), the deformation registrationmay include scaling component(s). FIG. 5 illustrates another example ofa volumetric image 500 that includes three image slices 502 a-502 c. Theimage slices 502 a-502 c are arranged in the Z-direction, whichcorresponds with the axis of a bore of an imaging machine (such as thedevice of FIG. 1) in some embodiments. In other embodiments, the imageslices may be arranged in other directions. Although three image slicesare shown, and each slice is illustrated as having sixteen pixels, inother examples, there may be more or less than three image slices, andthere may be more or less than sixteen pixels. In the example, the imageof an object 520 a (defined by pixel values that are higher than 0) isscaled by a factor of 2 in the Y-direction between image slice 502 a(e.g., a first image) and image slice 502 b (e.g., a second image). Theimage of the object 520 b (defined by pixel values that are higher than0) is scaled by a factor of 2 in the X-direction between image slice 502b and image slice 502 c. Thus, in this example, the processor maydetermine that the deformation registration between the image slices 502a and 502 b has a scaling component of Sy=2, and that the deformationregistration between the image slices 502 b and 502 c has a scalingcomponent of Sx=2. In such cases, the volumetric image 500 may becompressed by including the pixel values for the object 520 a from thefirst image slice 502 a, and the scaling factors (e.g., Sy=2, Sx=2) forthe respective image slices 502 b, 502 c, in a compressed image set(Step 206). The compressed image set may then be stored in a medium(Step 208).

In some embodiments, an image may include different image portions fordifferent respective objects. In such cases, the deformationregistration obtained in Step 204 may include one or a combination oftranslation component(s) (e.g., translation in one or more directions),rotation component(s), and scaling component(s) (e.g., scaling in one ormore directions), for each of the image portions in an image. Forexample, an image may include two image portions of two respectiveobjects (which for example, may be two different portions of an organ,or two different organs). In another image, one of the objects may movein the X-direction by 1 unit (ΔX=1), and the other one of the objectsmay move in the Y-direction by 3 units (ΔY=3) and in the X-direction by2 units (ΔX=2). As another example, a first image may include a firstobject that rotates clockwise (Δθ=−30°) compared to a correspondingobject in a second image, while another object in the same first imagemay rotate counter clockwise (Δθ=15°) compared to a corresponding objectin the same second image. As another example, a first image may includea first object that is scaled by (Sx=1.4 and Sy=2) compared to acorresponding object in a second image, while another object in the samefirst image may be scaled by (Sx=−1.3 and Sy=3) compared to acorresponding object in the same second image. Thus, a deformationregistration for mapping two images may include different components fordifferent parts of the same image. In some embodiments, the processormay be configured to perform image segmentation to divide the image intodifferent image portions. The image segmentation may be performed suchthat different image portions in one image may be mapped to differentimage portions in another image. The segmentation of one image may bedifferent from the segmentation of another image.

In the above examples, the different images are adjacent image slices(e.g., at different positions). In such cases, the image portions in therespective images correspond to different tissue (which may or may notbe belong to a same organ or class of tissue). As a result, thedeformable registration and image data compression technique describedabove is performed with respect to the space domain. In otherembodiments, the different images may be images from different phases ofa physiological cycle, in which cases, the image portions in therespective images correspond to a same tissue. As a result thedeformable registration and image data compression technique describedabove may be performed with respect to the time domain. In furtherembodiments, the above deformable registration and image compressiontechnique may be performed in both the space domain and time domain. Forexample, in the case in which the image data to be compressed is a setof volumetric images at different phases, such as a 4D CBCT set, theprocessor may compress the image data in the space domain for eachvolumetric image at a given phase (or phase range), and then compressthe image data in the time domain across the different volumetric imagesat different respective times.

In some embodiments, the inherent noise in the 4D image data set can bereduced by analyzing the pixel signal from adjacent phases and adjacentslices of the data set using the appropriate deformation field tocompute a space-time weighted average of the pixel values beforecompression. In some embodiments, an equal weighting may be applied forall voxels that are co-registered to the same reference voxel or voxellocation. Co-registered voxels may be from each common phase or phaserange in a respiratory image (e.g., 4D CT image) and/or from adjacentimages (e.g., adjacent images slices in a volumetric image, adjacentimage frames in a movie). In other embodiments, the distance betweenvoxels can be used to modulate the weighting. For example, the weightingfactor may be reduced the further away in distance or time a voxel isfrom another voxel (e.g., a reference voxel). In further embodiments,the uncertainty in the deformable registration can be used to determinethe weighting. For example, the less certain the mapping, the lower theweighing factor. Using images with a higher signal-to-noise ratio canresult in higher fidelity data compression in the time domain and spacedomain.

As illustrated in the above embodiments, the image data compression maybe performed in the time and space domain by tracking the motion of eachpixel from one frame to the next. For example, deformable imageregistration is able to map pixel motion from one image slice to thenext in a volumetric image set, and/or from one phase to the next in a4D CT data set. The motion vector field for the volumetric image set, orfor the 4D CT data set, can be used to compress the data set along thetime axis. Various techniques may be used by the processor to performdeformable image registration between different images to determine howone region in an image corresponds with another region in another image.The deformation registration (or the deformation field) resulted fromthe deformation registration technique that maps points from one imageto another image may include a rigid component, such as a translationcomponent, a rotation component, or a combination of both. For example,consider the following two images:

-   -   01200133000 first image    -   00012001300 second image        The deformation registration that maps these two images would        be:    -   22222221111        in which each number denotes how much the corresponding point in        the first image needs to be moved to the right in order to match        the second image. In some embodiments, the rigid component(s)        (e.g., translation component, rotation component) and/or the        scaling component in the deformation registration may be removed        to allow a user to evaluate how well an image matches another        image. Using the above example, the rigid component may be        removed from the deformation registration to obtain the        remaining deformation registration, as follows:

-   22222221111 Deformation registration

-   22222222222 Rigid component (shift image to right by two units)

-   0000000(−1)(−1)(−1)(−1) Remaining component in deformation    registration    The rigid component and the remaining component of the deformation    registration may be applied to the first image to accomplish the    second image, as follows:

-   01200133000 first image

-   00012001330 Applying rigid component (shift image to right by two    units)

-   00012001300 Applying remaining component (move latter part left by    one unit)    As shown in the above example, the region that comprises of 3's is    compressed to fit the second image. Thus, the deformation    registration contains a rigid component that moves the first image    to the right by two units, and a component that compresses the    region with 3s.

In some embodiments, the processor (e.g., the processor 54) isconfigured to determine how much deformation is needed to achieve amapping between images after the translation component(s), rotationcomponent(s), and scaling component(s) are removed from the deformationregistration. This would allow a user to know how much localized changein one image is needed in order to achieve the other image (e.g., theadjacent image slice, or the image in the adjacent phase). For example,in the above example, the processor may calculate that there is onepoint in the image where compression occurs. In another embodiment, theprocessor may calculate how much absolute movement is left in theremaining component in order to achieve the image being considered. Inthe above example, the processor would determine that there are fourcomponents (with value of −1) in the remaining component that are neededto be adjusted in order to achieve the second image.

It should be noted that the deformable registration technique that maybe used is not limited to the examples described, and that otherdeformable registration techniques may be used in different embodiments.Deformable image registration algorithms are known in the art, and willnot be described in further details. Also, it should be noted that anyof the deformation registration, the translation component(s), therotation component(s), and the scaling component(s) is not limited to atwo dimensional matrix illustrated in the above examples, and that inother embodiments, any of the deformation registration, the translationcomponent(s), the rotation component(s), and the scaling component(s)may be a three dimensional matrix. Further, it should be noted that theremaining deformation registration (i.e., the remaining component of adeformation registration) is not limited to having one localized changefor an image, and that in other embodiments, the remaining deformationfield may represent more than one localized changes in an image. Forexample, after the rigid component(s) and the scaling component(s) havebeen removed from the deformation registration, the remainingdeformation registration may indicate that a plurality of regions in animage needs to be adjusted in order to map to another image (e.g.,adjacent image slice, or image at an adjacent phase of a physiologicalcycle). One region may require compression, and another region mayrequire expansion. In another case, one region may require compressionby a first magnitude (e.g., 1 unit), and another region may requirecompression by a second magnitude (e.g., 3 units). Also, In some cases,the direction of compression/expansion in one region may be differentfrom the direction of compression/expansion in another region. Thus,embodiments of the deformation registration may include one or morecompression components, and/or one or more expansion components.

In some embodiments, the processor may remove the rotation, translation,and scaling components from the deformation field by finding the bestcombination of these components that would result in the smallest amountof change in the remaining deformation field. In some cases, thetranslation component may be determined by calculating the averagevector inside a region of interest.

In other embodiments, the processor may be configured to calculatedivergence and curl components of the deformation field for each point,convert those to absolute values, and integrate over the field. Theabove techniques provide information regarding how much different therelative positions of the biological reference points are in the images.They also provide information about where the differences are (e.g.,locations where the deformation field is divergent).

In any of the embodiments described herein, image compression may beperformed for pixel values that are in an image slice. For example, insome embodiments, after the deformable registration has been performed,the image data in an image (e.g., a reference image) may be “compressed”to further reduce information down to a smaller set. In some cases, theprocessor may be configured to examine the entire data set to be stored(e.g., time-sequence image data, and its corresponding registrationmatrix), and reduce the information that is redundant in order toachieve the data compression. Various techniques, such as a statisticaltechnique or a transformation into the frequency domain, may be used tocompress image data within an image. FIGS. 6A-6C illustrate a techniquefor compressing image data from a same image slice. FIG. 6A shows animage 700 that has four pixels in the X-direction, and four pixels inthe Y-direction. Thus, the image 700 is a 4×4 image with sixteen pixels702. In other examples, the image 700 may have more than sixteen pixels.The pixel P at the lower left corner of the image 700 has coordinate(1,1), and a pixel value of 2 (i.e., P_(1,1)=2). The pixel to the right(P_(2,1)) has intensity of 1. Similarly, the pixel P_(3,1) has intensityof 1, and pixel P_(4,1) has intensity of 0. The second row of the pixelsP_(x,2) all have intensity values of 2. The pixels in the third row,P_(1,3), P_(2,3), P_(3,3), P_(4,3) have respective intensity values of2, 2, 2, 1. The fourth row P_(1,4), P_(2,4), P_(3,4), P_(4,4) haverespective intensity values of 1, 1, 1, 1.

As shown in FIG. 6B, one technique of compressing the image data in theimage 700 involves analyzing the pixel values in the X-direction one rowat a time, starting with the first row, to determine if there are pixelsthat may be grouped together in the compression process. Starting withthe first row, P_(1,1) has intensity value of 2 that is different fromits adjacent pixel. Thus, P_(1,1) will not be grouped with the adjacentpixel. As a result, the data that correspond with the pixel P_(1,1) willbe stored as (2,1) in a compressed file under the format (v, n), whereinv is the value for the pixel, and n is the number of repeats (see firstset of values in FIG. 6C). The next two pixels P_(2,1) and P_(3,1) bothhave pixel values of 1, and so these two pixels will be groupedtogether. Thus, the data that correspond with these pixels will bestored as (1, 2) in the compressed file. The next pixel P_(4,1) haspixel value of 0, and so it will be stored as (0, 1) in the compressedfile.

After processing pixel values at the first row, the processor then moveson to the next row. The second row has four pixels P_(x,2) all withvalue of 2, and so these pixel values will be stored as (2, 4) in thecompressed file. Continuing with such data compression technique, theimage 700 of FIG. 6A may be represented by the compressed data shown inFIG. 6C. As shown in the example, the set of data in FIG. 6C hasfourteen values, which is less than the sixteen values of the sixteenpixels that would have been stored individually if compression techniqueis not used.

In other embodiments, instead of starting at the lower left corner of animage frame, the compression of the image data may begin at otherlocations (e.g., top left, top right, lower right, etc.).

Alternatively, the processing of the pixel values may be performed bythe processor in a left-and-right configuration. In such cases, theimage 700 of FIG. 6A may be analyzed as a continuous string of data.Using such technique, the first row of the image 700 would be processedfrom left-to-right, and yield the same compression results. Thus, (2,1), (1, 2), and (0, 1) would be stored in the compression data thatrepresent the pixel values in the first row. After processing the fourthpixel P_(4,1) in the first row, the processor then moves up and startswith pixel P_(4,2), and examines the remaining pixels in that row fromright-to-left. After the pixel P_(1,2) is processed, the processor hasregistered that there are four pixels with value of 2 in the second row.However, instead of including (2, 4) in the compressed data set, theprocessor continues to examine the next row starting with pixel P_(1,3)to see if it has the same pixel value. In the illustrated example, thepixel P_(1,3) to P_(3,3) all have a value of 2. Thus, the processor willdetermine that the pixels from P_(4,2) to P_(1,2) to P_(1,3) to P_(3,3)(see FIG. 7A) all have a value of 2. As a result the data set (2, 7)will be included in the compressed data set (FIG. 7B). Continue with theabove technique, the processor next determines that the remaining fivepixels from P_(4,3) to P_(4,4) to P_(1,4) all have a value of 1. Thus,the data set (1, 5) will be included in the compressed data set (FIG.7B). As shown in FIG. 7B, the compressed data set has a total of tenvalues, which provides a more effective data compression than theexample of FIG. 6C.

It should be noted that the above image data compression techniquedescribed with reference to FIGS. 6-7 is not limited to the X-direction,and that the same technique may be utilized to compress image data inany direction, such as the Y-direction. Also, in some embodiments, theadjacent pixels may have respective values that deviate within aprescribed range, and still be grouped together for the purpose ofcompressing the image data. For example, one pixel may have a value of2, and its adjacent pixel may have a value of 2.1. In such cases, thetwo pixels may be grouped together in the above described technique. Insome embodiments, a user interface may be provided that allows a user toprescribe a deviation range for grouping pixels. For example, if theuser prescribes that the deviation range is 4, then the processor willgroup two pixels if the difference in their respective pixel values isless than 4.

In any of the embodiments described herein, the compressed image set andthe registration matrices may be stored in a non-transitory medium. Thecompression technique described herein is beneficial because thedeformation registration matrices are used to further compress theimages, no matter what image compression method is used to compress eachimage individually. By using the deformation matrix to achievecompression, the resulting compressed image data set and the deformationmatrix will take up less memory/storage space (compared to the spacerequired to store the deformation matrix and the compressed image dataset that is compressed without using the deformation matrix). In someembodiments, the deformation registration matrices are themselvescompressed before they are stored. In other embodiments, the deformationregistration matrices are not compressed before they are stored.

Another aspect of the embodiments involves retrieving image data thatare stored as compressed image data, and performing a “de-compression”procedure to recover the compressed image. The de-compression may beperformed in time domain (e.g., for images at different phases).Alternatively, the de-compression may be performed in space domain(e.g., for adjacent image slices for a volumetric image). Alternatively,the de-compression may be performed in the frequency domain. FIG. 8illustrates a method 800 of decompressing image data that involvesdeformation registration in accordance with some embodiments. First,compressed image data are obtained (Step 802). In some embodiments, suchmay be accomplished by a processor (e.g., processor 54) receiving thecompressed image data, or by allowing the processor to have access to amedium that stores the compressed image data. In some embodiments, thecompressed image data may include compressed image data for a volumetricimage (e.g., a CT image, a tomosynthesis image, etc.). In such cases,the decompression method 800 would provide different image slices of thevolumetric image. In other embodiments, the compressed image data mayinclude image data that correspond with different respective phases of aphysiological cycle (such as a breathing cycle, a cardiac cycle, etc.)of a patient. In such cases, the decompression method 800 would providedifferent images from a video stream. Each of the images from the videostream may be a two-dimensional image, or a three-dimensional image. Infurther embodiments, the decompressed images may be images that werepreviously obtained at different times. Also, in other embodiments, thecompressed image data may include image data that are compressed in thespace domain and time domain.

Next, a deformation registration previously used to create thecompressed image data are obtained (Step 804). In some embodiments, suchmay be accomplished by a processor receiving the deformationregistration information, or by allowing the processor to have access toa medium that stores such information. In some embodiments, thedeformation registration is stored as a part of the compressed imagedata. In such cases, the Steps 802, 804 are combined, and the act ofretrieving the stored compressed image data also accomplishes theobtaining of the deformation registration.

Next, the processor performs image decompression on the compressed imagedata using the deformation registration to obtain decompressed imagedata (Step 806). In some embodiments, if the deformation registrationincludes information about a rigid translation of an image portion, theprocessor may use such information to create an image. Using the exampleof FIG. 3, the processor may determine that the deformation registrationfor image slice 302 b includes a rigid translation of ΔX=1. Thus,starting with the image 302 a, the processor then applies such ΔX forthe image portion 320 a (e.g., by shifting the image portion 320 a inthe X-direction by 1 unit) to create the image slice 302 b. Theprocessor then moves on to the next image slice. In the example, theprocessor may determine that the deformation registration for imageslice 302 c includes no rigid translation (ΔX=0). Thus, starting withthe image 302 b, the processor then applies such ΔX for the imageportion 320 b (e.g., by shifting the image portion 320 b in theX-direction by 0 unit) to create the image slice 302 c.

In some embodiments, if the deformation registration obtained in Step804 includes information about a scaling of an image portion, theprocessor may use such information to create an image. Using the exampleof FIG. 5, the processor may determine that the deformation registrationfor image slice 502 b includes a scaling component of Sy=2. Thus,starting with the image 502 a, the processor then applies such Sy factorfor the image portion 520 a (e.g., by scaling the image portion 520 a inthe Y-direction by 2) to create the image slice 502 b. The processorthen moves on to the next image slice. In the example, the processor maydetermine that the deformation registration for image slice 502 cincludes a scaling component of Sx=2. The processor then applies such Sxfactor for the image portion 520 b (e.g., by scaling the image portion520 b in the X-direction by 2) to create the image slice 502 c.

In some cases, if pixels with different respective pixel values areassociated with each other during the image compression, thedecompressed image may not have exactly the same pixel values as theoriginal uncompressed image. For example, if two pixels with respectivepixel values of “2” and “2.1” are associated during the imagecompression, when the processor decompresses the compressed image set,the processor may determine that both of the pixels have the same valueof “2.” This is acceptable as long as the different classes of tissuecan still be identified from the decompressed image.

In some embodiments, the deformation registration obtained in Step 804may include one or a combination of translation component(s) (e.g.,translation in one or more directions), rotation component(s), andscaling component(s) (e.g., scaling in one or more directions), for eachof the image portions in an image. Thus, a deformation registration formapping two images may include different components for different partsof the same image.

In some embodiments, if the compressed image data are obtained using thetechnique described with reference to FIGS. 7-8, the processor may usethe stored data (such as those shown in FIGS. 7B and 8B) to recreate theimage (decompressed image). In some cases, if pixels with differentrespective pixel values are grouped together during the imagecompression, the decompressed image may not have exactly the same pixelvalues as the original uncompressed image. For example, if two pixelswith respective pixel values of “2” and “2.1” are grouped during theimage compression, when the processor decompresses the compressed imageset, the processor may determine that both of the pixels have the samevalue of “2.” This is acceptable as long as the different classes oftissue can still be identified from the decompressed image.

After the decompressed image data are obtained, the decompressed imagedata may be used for a variety of purposes (Step 808). For example, insome embodiments, the decompressed image data may be displayed on ascreen for allowing a user (e.g., a physician, a treatment planner,etc.) to view the images. In other embodiments, the decompressed imagedata may be stored in a medium for processing.

In the above embodiments, the image data for compression anddecompression are CT or tomosynthesis images. In other embodiments, theimage data may be other types of images, such as MRI images (in whichcase, the system would involve using an MRI scanner), ultrasound images,x-ray images, PET images, SPECT images, etc, or any images that arecapable of being digitized. In such cases, the above image compressionand decompression techniques may be similarly applied for these types ofimages.

Computer System Architecture

FIG. 9 is a block diagram that illustrates an embodiment of a computersystem 1200 upon which an embodiment of the invention may beimplemented. Computer system 1200 includes a bus 1202 or othercommunication mechanism for communicating information, and a processor1204 coupled with the bus 1202 for processing information. The processor1204 may be an example of the processor 54 of FIG. 1, or anotherprocessor that is used to perform various functions described herein. Insome cases, the computer system 1200 may be used to implement functionsof the processor 54. The computer system 1200 also includes a mainmemory 1206, such as a random access memory (RAM) or other dynamicstorage device, coupled to the bus 1202 for storing information andinstructions to be executed by the processor 1204. The main memory 1206also may be used for storing temporary variables or other intermediateinformation during execution of instructions to be executed by theprocessor 1204. The computer system 1200 further includes a read onlymemory (ROM) 1208 or other static storage device coupled to the bus 1202for storing static information and instructions for the processor 1204.A data storage device 1210, such as a magnetic disk or optical disk, isprovided and coupled to the bus 1202 for storing information andinstructions.

The computer system 1200 may be coupled via the bus 1202 to a display1212, such as a cathode ray tube (CRT) or a flat panel, for displayinginformation to a user. An input device 1214, including alphanumeric andother keys, is coupled to the bus 1202 for communicating information andcommand selections to processor 1204. Another type of user input deviceis cursor control 1216, such as a mouse, a trackball, or cursordirection keys for communicating direction information and commandselections to processor 1204 and for controlling cursor movement ondisplay 1212. This input device typically has two degrees of freedom intwo axes, a first axis (e.g., x) and a second axis (e.g., y), thatallows the device to specify positions in a plane.

The computer system 1200 may be used for performing various functions(e.g., calculation) in accordance with the embodiments described herein.According to one embodiment, such use is provided by computer system1200 in response to processor 1204 executing one or more sequences ofone or more instructions contained in the main memory 1206. Suchinstructions may be read into the main memory 1206 from anothercomputer-readable medium, such as storage device 1210. Execution of thesequences of instructions contained in the main memory 1206 causes theprocessor 1204 to perform the process steps described herein. One ormore processors in a multi-processing arrangement may also be employedto execute the sequences of instructions contained in the main memory1206. In alternative embodiments, hard-wired circuitry may be used inplace of or in combination with software instructions to implement theinvention. Thus, embodiments of the invention are not limited to anyspecific combination of hardware circuitry and software.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing instructions to the processor 1204 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media, volatile media, and transmission media.Non-volatile media includes, for example, optical or magnetic disks,such as the storage device 1210. Volatile media includes dynamic memory,such as the main memory 1206. Transmission media includes coaxialcables, copper wire and fiber optics, including the wires that comprisethe bus 1202. Transmission media can also take the form of acoustic orlight waves, such as those generated during radio wave and infrared datacommunications.

Common forms of computer-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, or any other magneticmedium, a CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, a RAM, a PROM, and EPROM,a FLASH-EPROM, any other memory chip or cartridge, a carrier wave asdescribed hereinafter, or any other medium from which a computer canread.

Various forms of computer-readable media may be involved in carrying oneor more sequences of one or more instructions to the processor 1204 forexecution. For example, the instructions may initially be carried on amagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to the computer system 1200can receive the data on the telephone line and use an infraredtransmitter to convert the data to an infrared signal. An infrareddetector coupled to the bus 1202 can receive the data carried in theinfrared signal and place the data on the bus 1202. The bus 1202 carriesthe data to the main memory 1206, from which the processor 1204retrieves and executes the instructions. The instructions received bythe main memory 1206 may optionally be stored on the storage device 1210either before or after execution by the processor 1204.

The computer system 1200 also includes a communication interface 1218coupled to the bus 1202. The communication interface 1218 provides atwo-way data communication coupling to a network link 1220 that isconnected to a local network 1222. For example, the communicationinterface 1218 may be an integrated services digital network (ISDN) cardor a modem to provide a data communication connection to a correspondingtype of telephone line. As another example, the communication interface1218 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, the communication interface1218 sends and receives electrical, electromagnetic or optical signalsthat carry data streams representing various types of information.

The network link 1220 typically provides data communication through oneor more networks to other devices. For example, the network link 1220may provide a connection through local network 1222 to a host computer1224 or to equipment 1226 such as a radiation beam source or a switchoperatively coupled to a radiation beam source. The data streamstransported over the network link 1220 can comprise electrical,electromagnetic or optical signals. The signals through the variousnetworks and the signals on the network link 1220 and through thecommunication interface 1218, which carry data to and from the computersystem 1200, are exemplary forms of carrier waves transporting theinformation. The computer system 1200 can send messages and receivedata, including program code, through the network(s), the network link1220, and the communication interface 1218.

Although particular embodiments have been shown and described, it willbe understood that they are not intended to limit the presentinventions, and it will be obvious to those skilled in the art thatvarious changes and modifications may be made without departing from thespirit and scope of the present inventions. The specification anddrawings are, accordingly, to be regarded in an illustrative rather thanrestrictive sense. The present inventions are intended to coveralternatives, modifications, and equivalents, which may be includedwithin the spirit and scope of the present inventions as defined by theclaims.

What is claimed:
 1. A method of processing image data, comprising:obtaining an image set that includes at least a first image and a secondimage; determining a deformation registration using the first and secondimages, wherein the act of determining the deformation registration isperformed using a processor; performing data compression on at least aportion of the image set using the determined deformation registrationto obtain compressed image data, wherein the compressed image data hasless storage requirement compared to a storage requirement of the atleast a portion of the image set before the data compression isperformed; and storing the compressed image data.
 2. The method of claim1, wherein the first and second images are parts of a volumetric CTimage.
 3. The method of claim 1, wherein the first and second images areparts of a sequence of images.
 4. The method of claim 1, wherein thefirst image corresponds to a first phase of a physiological cycle of thepatient, and the second image corresponds to a second phase of thephysiological cycle of the patient, the second phase being differentfrom the first phase.
 5. The method of claim 1, wherein the deformationregistration comprises a translational component, a rotationalcomponent, a scaling component, a combination of stretching andcompression components, or a combination thereof.
 6. The method of claim1, wherein the deformation registration is determined by: obtaining fromthe first image a first plurality of segments; obtaining from the secondimage a second plurality of segments; and mapping each of the firstplurality of segments from the first image with a corresponding one ofthe second plurality of segments from the second image.
 7. The method ofclaim 1, wherein the first image comprises a CT image, a PET image, aSPECT image, a PET-CT image, a tomosynthesis image, a digitizedultrasound image, a digitized fluoroscope image, or a digitized MRIimage.
 8. The method of claim 1, further comprising storing thedeformation registration.
 9. A system for processing image data,comprising: a processor configured for obtaining an image set thatincludes at least a first image and a second image, determining adeformation registration using the first and second images, andperforming data compression on at least a portion of the image set usingthe determined deformation registration to obtain compressed image data,wherein the compressed image data has less storage requirement comparedto a storage requirement of the at least a portion of the image setbefore the data compression is performed, and a non-transitory mediumfor storing the compressed image data.
 10. The system of claim 9,wherein the deformation registration comprises a translationalcomponent, a rotational component, a scaling component, or a combinationthereof.
 11. The system of claim 9, wherein the processor is configuredto determine the deformation registration by: obtaining from the firstimage a first plurality of segments; obtaining from the second image asecond plurality of segments; and mapping each of the first plurality ofsegments from the first image with a corresponding one of the secondplurality of segments from the second image.
 12. The system of claim 9,wherein the non-transitory medium is configured to store the deformationregistration.
 13. A computer product having a volatile or non-volatilemedium that stores a set of instruction, an execution of which by aprocessor causes a method for processing image data to be performed, themethod comprising: obtaining an image set that includes at least a firstimage and a second image; determining a deformation registration usingthe first and second images; performing data compression on at least aportion of the image set using the determined deformation registrationto obtain compressed image data, wherein the compressed image data hasless storage requirement compared to a storage requirement of the atleast a portion of the image set before the data compression isperformed; and storing the compressed image data.
 14. The computerproduct of claim 13, wherein the deformation registration comprises atranslational component, a rotational component, a scaling component, acombination of stretching and compression component, or a combinationthereof.
 15. The computer product of claim 13, wherein the deformationregistration is determined by: obtaining from the first image a firstplurality of segments; obtaining from the second image a secondplurality of segments; and mapping each of the first plurality ofsegments from the first image with a corresponding one of the secondplurality of segments from the second image.
 16. The computer product ofclaim 13, wherein the method further comprises storing the deformationregistration.
 17. A method of processing image data, comprising:obtaining compressed image data; obtaining a deformation registrationpreviously used to create the compressed image data; performing datadecompression on the compressed image data using the deformationregistration to obtain decompressed image data, wherein the act ofperforming the data decompression is performed using a processor, andwherein the compressed image data has less storage requirement comparedto a storage requirement of the decompressed image data; and storing thedecompressed image data.
 18. The method of claim 17, wherein thedecompressed image data is a part of a volumetric CT image.
 19. Themethod of claim 17, wherein the decompressed image data is a part of asequence of images.
 20. The method of claim 17, wherein the decompressedimage data comprises a first image that corresponds to a first phase ofa physiological cycle of the patient, and a second image thatcorresponds to a second phase of the physiological cycle of the patient,the second phase being different from the first phase.
 21. The method ofclaim 17, wherein the deformation registration comprises a translationalcomponent, a rotational component, a scaling component, a combination ofstretching and compression components, or a combination thereof.
 22. Themethod of claim 17, wherein the decompressed image data includes a firstimage and a second image, and wherein the deformation registrationincludes information for mapping each of a first plurality of segmentsfrom the first image with a corresponding one of a second plurality ofsegments from the second image.
 23. A system for processing image data,comprising: a processor configured for obtaining compressed image data,obtaining a deformation registration previously used to create thecompressed image data, and performing data decompression on thecompressed image data using the deformation registration to obtaindecompressed image data, wherein the compressed image data has lessstorage requirement compared to a storage requirement of thedecompressed image data; and a non-transitory medium for storing thedecompressed image data.
 24. The system of claim 23, wherein thedeformation registration comprises a translational component, arotational component, a scaling component, or a combination thereof. 25.The system of claim 23, wherein the decompressed image data includes afirst image and a second image, and wherein the deformation registrationincludes information for mapping each of a first plurality of segmentsfrom the first image with a corresponding one of a second plurality ofsegments from the second image.
 26. A computer product having a volatileor non-volatile medium that stores a set of instruction, an execution ofwhich by a processor causes a method for processing image data to beperformed, the method comprising: obtaining compressed image data;obtaining a deformation registration previously used to create thecompressed image data; performing data decompression on the compressedimage data using the deformation registration to obtain decompressedimage data, wherein the compressed image data has less storagerequirement compared to a storage requirement of the decompressed imagedata; and storing the decompressed image data.
 27. The computer productof claim 26, wherein the deformation registration comprises atranslational component, a rotational component, a scaling component, acombination of stretching and compression components, or a combinationthereof.
 28. The computer product of claim 26, wherein the decompressedimage data includes a first image and a second image, and wherein thedeformation registration includes information for mapping each of afirst plurality of segments from the first image with a correspondingone of a second plurality of segments from the second image.